From 9601286edb74b9eff4d64571834e559333160f8c Mon Sep 17 00:00:00 2001 From: Andreas Motl Date: Tue, 16 Sep 2025 07:32:23 +0200 Subject: [PATCH 1/2] Naming things: Use "time series" instead of "time-series" https://en.wikipedia.org/wiki/Time_series --- docs/feature/cluster/index.md | 4 ++-- docs/feature/search/geo/index.md | 6 +++--- docs/home/index.md | 6 +++--- docs/integrate/kafka/index.md | 4 ++-- docs/integrate/superset/index.md | 4 ++-- docs/start/first-steps.md | 4 ++-- docs/start/modelling/fulltext.md | 2 +- docs/start/modelling/geospatial.md | 4 ++-- docs/start/modelling/relational.md | 2 +- docs/start/modelling/timeseries.md | 2 +- docs/start/query/ad-hoc.md | 2 +- docs/start/query/aggregations.md | 2 +- docs/start/query/performance.md | 22 +++++++++++----------- 13 files changed, 32 insertions(+), 32 deletions(-) diff --git a/docs/feature/cluster/index.md b/docs/feature/cluster/index.md index 835c8878..75b2af93 100644 --- a/docs/feature/cluster/index.md +++ b/docs/feature/cluster/index.md @@ -171,12 +171,12 @@ Guidelines about balancing your strategy to yield the best performance for your :::{grid-item-card} :link: https://community.cratedb.com/t/sharding-and-partitioning-guide-for-time-series-data/737 -:link-alt: Sharding and partitioning guide for time-series data +:link-alt: Sharding and partitioning guide for time series data :padding: 3 :class-header: sd-text-center sd-fs-5 sd-align-minor-center sd-font-weight-bold :class-body: sd-text-center2 sd-fs2-5 :class-footer: text-smaller -Sharding and partitioning guide for time-series data +Sharding and partitioning guide for time series data ^^^ A hands-on walkthrough to support you with building a sharding and partitioning strategy for your time series data. diff --git a/docs/feature/search/geo/index.md b/docs/feature/search/geo/index.md index 9ef1e3af..05eec139 100644 --- a/docs/feature/search/geo/index.md +++ b/docs/feature/search/geo/index.md @@ -189,11 +189,11 @@ tutorials, or example applications. **Getting Started with Geospatial Data in CrateDB** Discover how to effortlessly create a table and seamlessly import weather -data into CrateDB in this video. Witness the power of CrateDB's time-series +data into CrateDB in this video. Witness the power of CrateDB's time series query capabilities in action with a weather dataset, showcasing the dynamic schema flexibility. -[CrateDB: Querying Multi-Model Heterogeneous Time-Series Data with SQL] +[CrateDB: Querying Multi-Model Heterogeneous Time Series Data with SQL] Dive deeper into CrateDB's multi-modal features with demonstrations on handling JSON, geospatial data, and conducting full-text searches. @@ -263,7 +263,7 @@ flip dot sign. The software is written in JavaScript and runs on Node.js. [Apache Solr Spatial Search]: https://solr.apache.org/guide/solr/latest/query-guide/spatial-search.html [Berlin and Geo Shapes in CrateDB]: https://cratedb.com/blog/geo-shapes-in-cratedb -[CrateDB: Querying Multi-Model Heterogeneous Time-Series Data with SQL]: https://cratedb.com/resources/videos/unleashing-the-power-of-multi-model-data-querying-heterogeneous-time-series-data-with-sql-in-cratedb +[CrateDB: Querying Multi-Model Heterogeneous Time Series Data with SQL]: https://cratedb.com/resources/videos/unleashing-the-power-of-multi-model-data-querying-heterogeneous-time-series-data-with-sql-in-cratedb [GeoJSON]: https://en.wikipedia.org/wiki/GeoJSON [Geometric Shapes Indexing with BKD-trees]: https://cratedb.com/blog/geometric-shapes-indexing-with-bkd-trees [Geospatial Indexing & Search at Scale with Lucene]: https://portal.ogc.org/files/?artifact_id=90337 diff --git a/docs/home/index.md b/docs/home/index.md index 704f5ef1..e6bbf51a 100644 --- a/docs/home/index.md +++ b/docs/home/index.md @@ -6,7 +6,7 @@ orphan: true # Welcome to CrateDB CrateDB is a **distributed SQL database** designed for **real-time analytics -and search** at scale. Whether you are working with time-series data, full-text +and search** at scale. Whether you are working with time series data, full-text search, or large volumes of structured and semi-structured data, CrateDB gives you the **power of SQL**, the **scalability of NoSQL**, and the **flexibility of a modern data platform**. @@ -26,7 +26,7 @@ CrateDB was built for speed, scale, and simplicity: * **Real-time performance:** Query millions of records per second with sub-second response times. * **AI/ML-ready:** Store and serve data for modern AI pipelines. * **Search + SQL**: Combine full-text search with rich SQL queries. -* **Geospatial & time-series**: Native support for IoT, sensor data, and location-based use cases. +* **Geospatial & time series**: Native support for IoT, sensor data, and location-based use cases. * **Horizontal scalability**: Add nodes effortlessly to handle more data and users. * **Resilient and fault-tolerant**: Built-in replication and recovery. :::: @@ -60,7 +60,7 @@ real-time analytics and hybrid search applications that leverage CrateDB's unique features. * In a unified data platform, CrateDB lets you analyze relational, JSON, - time-series, geospatial, full-text, and vector data in a single system, + time series, geospatial, full-text, and vector data in a single system, eliminating the need for multiple databases. * The fully distributed SQL query engine, built on top of Apache Lucene, and inheriting technologies from Elasticsearch/OpenSearch, provides performant diff --git a/docs/integrate/kafka/index.md b/docs/integrate/kafka/index.md index 9121e000..8aa216bd 100644 --- a/docs/integrate/kafka/index.md +++ b/docs/integrate/kafka/index.md @@ -27,7 +27,7 @@ Apache Kafka is a widely used open-source distributed event-store and streaming ## Overview -[Apache Kafka] is a distributed event log for high-throughput, durable, and scalable data streams. CrateDB is a distributed SQL database optimized for time-series, IoT, and analytics at scale. Together, they form a robust pipeline for moving operational events from producers into a queryable store with SQL and real-time analytics. +[Apache Kafka] is a distributed event log for high-throughput, durable, and scalable data streams. CrateDB is a distributed SQL database optimized for time series, IoT, and analytics at scale. Together, they form a robust pipeline for moving operational events from producers into a queryable store with SQL and real-time analytics. ## Benefits of CrateDB + Apache Kafka @@ -62,7 +62,7 @@ The processed results are then written into CrateDB, where they’re immediately ## Typical use cases -* **Time-series pipelines (sensors, logs, metrics, events)** +* **Time series pipelines (sensors, logs, metrics, events)** Stream high-volume data from IoT devices, application logs, or monitoring systems into Kafka, then land it in CrateDB for storage and real-time querying. Ideal for scenarios where you need to keep years of historical data but still run live analytics on the latest events. * **CDC / operational data feeds (Debezium → Kafka → CrateDB)** diff --git a/docs/integrate/superset/index.md b/docs/integrate/superset/index.md index 526111ac..3816f903 100644 --- a/docs/integrate/superset/index.md +++ b/docs/integrate/superset/index.md @@ -119,11 +119,11 @@ this video educates about at all the basic surfaces and workflows of Apache Supe :::{grid-item} :columns: auto auto 8 8 -**Apache Superset and CrateDB: Introduction to Time-Series Visualization** +**Apache Superset and CrateDB: Introduction to Time Series Visualization** In this webinar, we will discuss how to use different visualization options in Superset coupled with a SQL interface to derive interesting insights and findings -from the time-series dataset. +from the time series dataset. - [Introduction to time series visualization in CrateDB and Apache Superset (Webinar)] ::: diff --git a/docs/start/first-steps.md b/docs/start/first-steps.md index 25efd4f7..272a4fb9 100644 --- a/docs/start/first-steps.md +++ b/docs/start/first-steps.md @@ -29,10 +29,10 @@ or run them directly in the CrateDB Cloud Console. Learn how to use CrateDB’s full‑text search to explore a large dataset and manage a Netflix title catalog. -* **Exploring time-series data?** Investigate weather data — see {{ '{}(#timeseries-querying)'.format(tutorial) }} +* **Exploring time series data?** Investigate weather data — see {{ '{}(#timeseries-querying)'.format(tutorial) }} In this tutorial, you’ll work with weather readings from multiple locations - to learn how to efficiently store and analyze time-series datasets. + to learn how to efficiently store and analyze time series datasets. ## 3. Take an advanced tutorial diff --git a/docs/start/modelling/fulltext.md b/docs/start/modelling/fulltext.md index 2e9ab927..c7ac7f53 100644 --- a/docs/start/modelling/fulltext.md +++ b/docs/start/modelling/fulltext.md @@ -4,7 +4,7 @@ CrateDB offers **native full-text search** powered by **Apache Lucene** and Okapi BM25 ranking, accessible via SQL for easy modelling and querying of large-scale textual data. It supports fuzzy matching, multi-language analysis, and composite -indexing, while fully integrating with data types such as JSON, time-series, +indexing, while fully integrating with data types such as JSON, time series, geospatial, vectors, and more for comprehensive multi-model queries. Whether you need document search, catalog lookup, or content analytics, CrateDB is an ideal solution. diff --git a/docs/start/modelling/geospatial.md b/docs/start/modelling/geospatial.md index 0ff74f8e..efaa04fc 100644 --- a/docs/start/modelling/geospatial.md +++ b/docs/start/modelling/geospatial.md @@ -4,7 +4,7 @@ CrateDB supports **real-time geospatial analytics at scale**, enabling you to store, query, and analyze 2D location-based data using standard SQL over two dedicated types: **GEO\_POINT** and **GEO\_SHAPE**. You can seamlessly combine -spatial data with full-text, vector, JSON, or time-series in the same SQL +spatial data with full-text, vector, JSON, or time series in the same SQL queries. The strength of CrateDB's support for geospatial data includes: @@ -12,7 +12,7 @@ The strength of CrateDB's support for geospatial data includes: * Designed for **real-time geospatial tracking and analytics** (e.g., fleet tracking, mapping, location-layered apps) * **Unified SQL platform**: spatial data can be combined with full-text search, - JSON, vectors, time-series — in the same table or query + JSON, vectors, time series — in the same table or query * **High ingest and query throughput**, suitable for large-scale location-based workloads diff --git a/docs/start/modelling/relational.md b/docs/start/modelling/relational.md index 314b5fdb..323db006 100644 --- a/docs/start/modelling/relational.md +++ b/docs/start/modelling/relational.md @@ -5,7 +5,7 @@ CrateDB is a **distributed SQL database** that offers rich **relational data modelling** with the flexibility of dynamic schemas and the scalability of NoSQL systems. It supports **primary keys,** **joins**, **aggregations**, and **subqueries**, just like traditional RDBMS systems—while also enabling hybrid -use cases with time-series, geospatial, full-text, vector search, and +use cases with time series, geospatial, full-text, vector search, and semi-structured data. Use CrateDB when you need to scale relational workloads horizontally while diff --git a/docs/start/modelling/timeseries.md b/docs/start/modelling/timeseries.md index b2d66f62..2f155efd 100644 --- a/docs/start/modelling/timeseries.md +++ b/docs/start/modelling/timeseries.md @@ -117,7 +117,7 @@ ORDER BY expected_time; ``` -### Typical time-series functions +### Typical time series functions * **Time extraction:** `date_trunc(...)`, `extract(...)`, `date_part(...)`, `now()`, `current_timestamp` * **Time bucketing:** `date_bin()`, `interval`, `age()` diff --git a/docs/start/query/ad-hoc.md b/docs/start/query/ad-hoc.md index 5da5f9cb..ad83c11e 100644 --- a/docs/start/query/ad-hoc.md +++ b/docs/start/query/ad-hoc.md @@ -237,7 +237,7 @@ Learn more about how to use ad-hoc queries effectively. | Feature | Description | Documentation | |---------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------| | Dynamic schemas &
object columns | Flexible modeling of semi-structured JSON data
No need to predefine every field | {ref}`object`
{ref}`crate-reference:data-types-objects` | -| Time-series support | Perfect for time-bound diagnostics | {ref}`timeseries` | +| Time series support | Perfect for time-bound diagnostics | {ref}`timeseries` | | Intelligent indexing | Works out of the box for ad-hoc querying | {ref}`search-overview` | | Full-text & filter | Combine keyword search with structured queries | {ref}`fts`
{ref}`crate-reference:fulltext-indices` | diff --git a/docs/start/query/aggregations.md b/docs/start/query/aggregations.md index 5b8ed011..c360a3e8 100644 --- a/docs/start/query/aggregations.md +++ b/docs/start/query/aggregations.md @@ -22,7 +22,7 @@ Whether you are monitoring sensor networks, analyzing customer behavior, or powe ::: - Aggregate over **high-ingestion** datasets (millions of records per hour) -- Analyze **real-time** metrics across structured, JSON, or time-series fields +- Analyze **real-time** metrics across structured, JSON, or time series fields - Build **dynamic dashboards** and run **interactive ad-hoc analytics** - Combine aggregations with **full-text**, **geospatial**, or **vector** filters diff --git a/docs/start/query/performance.md b/docs/start/query/performance.md index cbe89f37..60971971 100644 --- a/docs/start/query/performance.md +++ b/docs/start/query/performance.md @@ -5,15 +5,15 @@ Follow these tips to use CrateDB optimally for maximum performance. ::: -| Optimization | Description | Documentation | -|--------------------------------|--------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| Leverage indexes | Important for frequently grouped or filtered fields.
Fields are indexed by default. | {ref}`performance-optimization` | -| Avoid SELECT \* | Select only the fields you need. | {ref}`performance-optimization` | -| Use targeted filters | Narrow your search using `WHERE` clauses.
Use time filters especially on time-series or partitioned tables. | {ref}`crate-reference:sql_dql_where_clause`
{ref}`crate-reference:comparison-operators` | -| Pre-aggregate | Maintain rollup tables for common queries; use views as convenient wrappers (views are virtual, not precomputed). | | -| Use `DATE_BIN` or `DATE_TRUNC` | Apply time-based bucketing on time-series data to reduce data volume. | {ref}`DATE_BIN() `
{ref}`DATE_TRUNC() `
[Optimizing storage for historic time-series data]
[Resampling time-series data with DATE_BIN] | -| Profile queries | Use `EXPLAIN` and `ANALYZE` to inspect performance. | {ref}`EXPLAIN `
{ref}`ANALYZE ` | -| Sizing & sharding | Choose partitioning and shard size wisely (e.g., daily partitions for time-based data). | {ref}`sharding-partitioning`
{ref}`sharding-performance` | +| Optimization | Description | Documentation | +|--------------------------------|-------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Leverage indexes | Important for frequently grouped or filtered fields.
Fields are indexed by default. | {ref}`performance-optimization` | +| Avoid SELECT \* | Select only the fields you need. | {ref}`performance-optimization` | +| Use targeted filters | Narrow your search using `WHERE` clauses.
Use time filters especially on time series or partitioned tables. | {ref}`crate-reference:sql_dql_where_clause`
{ref}`crate-reference:comparison-operators` | +| Pre-aggregate | Maintain rollup tables for common queries; use views as convenient wrappers (views are virtual, not precomputed). | | +| Use `DATE_BIN` or `DATE_TRUNC` | Apply time-based bucketing on time series data to reduce data volume. | {ref}`DATE_BIN() `
{ref}`DATE_TRUNC() `
[Optimizing storage for historic time series data]
[Resampling time series data with DATE_BIN] | +| Profile queries | Use `EXPLAIN` and `ANALYZE` to inspect performance. | {ref}`EXPLAIN `
{ref}`ANALYZE ` | +| Sizing & sharding | Choose partitioning and shard size wisely (e.g., daily partitions for time-based data). | {ref}`sharding-partitioning`
{ref}`sharding-performance` | :::{important} @@ -21,5 +21,5 @@ For in-depth details about performance aspects, please head over to the {ref}`pe ::: -[Optimizing storage for historic time-series data]: https://community.cratedb.com/t/optimizing-storage-for-historic-time-series-data/762 -[Resampling time-series data with DATE_BIN]: https://community.cratedb.com/t/resampling-time-series-data-with-date-bin/1009 +[Optimizing storage for historic time series data]: https://community.cratedb.com/t/optimizing-storage-for-historic-time-series-data/762 +[Resampling time series data with DATE_BIN]: https://community.cratedb.com/t/resampling-time-series-data-with-date-bin/1009 From c19c87b7ba4f49b247e4d3527fca161344b22855 Mon Sep 17 00:00:00 2001 From: Andreas Motl Date: Tue, 16 Sep 2025 08:11:54 +0200 Subject: [PATCH 2/2] Naming things: Fix remaining "time-series" as suggested by CodeRabbit --- docs/integrate/superset/index.md | 2 +- docs/start/first-steps.md | 2 +- docs/start/modelling/fulltext.md | 4 ++-- docs/start/query/aggregations.md | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/integrate/superset/index.md b/docs/integrate/superset/index.md index 3816f903..bc59e7d5 100644 --- a/docs/integrate/superset/index.md +++ b/docs/integrate/superset/index.md @@ -99,7 +99,7 @@ Introduction to time‑series visualization in CrateDB and Apache Superset. **Apache Superset 101** From connecting databases to building charts, dashboards, and interactive filters, -this video educates about at all the basic surfaces and workflows of Apache Superset. +this video covers all the basic surfaces and workflows of Apache Superset. ::: :::{grid-item} diff --git a/docs/start/first-steps.md b/docs/start/first-steps.md index 272a4fb9..c17f6f75 100644 --- a/docs/start/first-steps.md +++ b/docs/start/first-steps.md @@ -39,7 +39,7 @@ or run them directly in the CrateDB Cloud Console. * **Analyze device readings** with metadata integration — see {{ '{}(#timeseries-objects)'.format(tutorial) }} In this tutorial, capture device metrics such as battery level, CPU usage, - and memory usage, then enrich your time‑series data with JSON and text + and memory usage, then enrich your time series data with JSON and text metadata to enable more comprehensive analysis. diff --git a/docs/start/modelling/fulltext.md b/docs/start/modelling/fulltext.md index c7ac7f53..75e8c9b8 100644 --- a/docs/start/modelling/fulltext.md +++ b/docs/start/modelling/fulltext.md @@ -151,8 +151,8 @@ constraints, all in one. walkthrough of full-text search capabilities. * Reference Manual: * {ref}`Full-text indices `: Defining - indices, extending builtin analyzers, custom analyzers. - * {ref}`Full-text analyzers `: Builtin + indices, extending built-in analyzers, custom analyzers. + * {ref}`Full-text analyzers `: Built-in analyzers, tokenizers, token and char filters. * {ref}`SQL MATCH predicate `: Details about MATCH predicate arguments and options. diff --git a/docs/start/query/aggregations.md b/docs/start/query/aggregations.md index c360a3e8..befad2a9 100644 --- a/docs/start/query/aggregations.md +++ b/docs/start/query/aggregations.md @@ -207,7 +207,7 @@ GROUP BY status; CrateDB integrates seamlessly with: -:Grafana: Build real-time dashboards with time-series aggregations +:Grafana: Build real-time dashboards with time series aggregations :Apache Superset: Explore multidimensional data visually :Tableau, Power BI, Metabase: Connect via PostgreSQL wire protocol